Self-weighted alternating normalized residue fitting algorithm with application to quantitative analysis of excitation-emission matrix fluorescence data

摘要

In this paper, a novel algorithm named as self-weighted alternating normalized residue fitting (SWANRF) has been proposed for quantitative analysis of excitation-emission matrix fluorescence data. The proposed algorithm can obtain satisfactory solutions for the analytes of interest even in the presence of potentially unknown interferences, fully exploiting the second-order advantage. By comparing the performance of the alternating trilinear decomposion (ATLD) algorithm, and PARAFAC-ALS on one simulated and two real fluorescence spectral data arrays, SWANRF can deal with higher collinearity problems, obtain improved convergence rate through shuffling the computational matrices, and partially reextract valid information from the residue and further remove invalid information to the residue. In addition, SWANRF can only behave more stably, independent of the used initial values unlike PARAFAC, but also achieves very smooth profiles at high noise level, where ATLD may be helpless with the actual component and has to resort to additional component(s) to fit noise, yielding rough profiles. Based on these attractive merits, such a novel method may hold great potential to be extended as a promising alternative for three-way data array analysis.